R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 3189 articles , 6 standard articles )

Showing results 1 to 20 of 3189.
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  1. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  2. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  3. Agasisti, Tommaso; Ieva, Francesca; Paganoni, Anna Maria: Heterogeneity, school-effects and the north/south achievement gap in Italian secondary education: evidence from a three-level mixed model (2017)
  4. Albatineh, Ahmed N.; Boubakari, Ibrahimou; Kibria, B.M.Golam: New confidence interval estimator of the signal-to-noise ratio based on asymptotic sampling distribution (2017)
  5. Amatya, Anup; Demirtas, Hakan: PoisNor: an R package for generation of multivariate data with Poisson and normal marginals (2017)
  6. Andersson, Björn; Wiberg, Marie: Item response theory observed-score kernel equating (2017)
  7. Andrew Zammit-Mangion, Noel Cressie: FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets (2017) arXiv
  8. Angelov, Angel G.; Ekström, Magnus: Nonparametric estimation for self-selected interval data collected through a two-stage approach (2017)
  9. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  10. Antony Overstall, David Woods, Maria Adamou: acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange (2017) arXiv
  11. Arangala, Crista; Yokley, Karen A.: Exploring calculus. Labs and projects with Mathematica (2017)
  12. Arcagni, Alberto: On the decomposition by sources of the zenga 1984 point and synthetic inequality indexes (2017)
  13. Art B. Owen: A randomized Halton algorithm in R (2017) arXiv
  14. Asar, Özgür; Ilk, Ozlem; Dag, Osman: Estimating Box-Cox power transformation parameter via goodness-of-fit tests (2017)
  15. Ashley Petersen, Noah Simon, Daniela Witten: SCALPEL: Extracting Neurons from Calcium Imaging Data (2017) arXiv
  16. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  17. Baharev, Ali; Schichl, Hermann; Rév, Endre: Computing the noncentral-$F$ distribution and the power of the $F$-test with guaranteed accuracy (2017)
  18. Balan, Raluca; Lamothe, Gilles: Expect the unexpected. A first course in biostatistics. (2017)
  19. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  20. Beckerman, Andrew P.; Petchey, Owen L.: Getting started with R. An introduction for biologists. (2017)

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Further publications can be found at: http://journal.r-project.org/